Technical and economic effects of an inline progesterone indicator in a dairy herd estimated by stochastic simulation

Theriogenology 64 (2005) 819–843 www.journals.elsevierhealth.com/periodicals/the

Technical and economic effects of an inline progesterone indicator in a dairy herd estimated by stochastic simulation S. Østergaard *, N.C. Friggens, M.G.G. Chagunda Department of Animal Health, Welfare and Nutrition, Danish Institute of Agricultural Sciences, P.O. Box 50, Tjele DK-8830, Denmark Received 4 May 2004; received in revised form 29 September 2004; accepted 6 October 2004

Abstract For several years progesterone in milk or blood has been recognized as an indicator of different cow states related to reproduction. For this study, an existing simulation model was modified in order to analyze the technical and economic effects of including information on progesterone status in an automatic inline monitoring system. Implementation of an inline progesterone indicator was assumed to directly affect the estrus detection rate, the period until treatment for post-partum anestrus and the number of mistimed AIs. Different implementations of an inline progesterone indicator were simulated in a typical Danish dairy herd with 120 cows and in other herd situations represented by: a herd with poor reproduction efficiency, a herd with a high estrus detection rate and a herd using a 9 week postponed AI period for primiparous cows. It was concluded that implementation of an inline progesterone indicator in a dairy herd previously using visual estrus detection has a break-even price of s3–81 per cow-year depending on differences in implementation type and herd reproduction management. The highest break-even price was found using the assumptions that simulated a herd with initially poor reproductive efficiency. With the assumptions that simulated a typical Danish herd

* Corresponding author. Tel.: +45 8999 1304; fax: +45 8999 1500. E-mail address: [email protected] (S. Østergaard). 0093-691X/$ – see front matter # 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.theriogenology.2004.10.022

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the break-even price was s46 per cow-year. Attributable effects of using the indicator, including effects of labor time, are discussed. # 2005 Elsevier Inc. All rights reserved. Keywords: Progesterone; Stochastic simulation; Dairy cows

1. Introduction Reproductive efficiency in terms of interval from calving to first estrus and conception rate in dairy cows is decreasing worldwide and is approaching a critical level in many herds [1,2]. In the United States, the conception rate has been reported to decrease by 0.45% per year over a 20-year period [3]. In the UK this decrease has been in order of 1% per year [1]. This negative trend seems to be related to changes in cows, management and housing factors. Some of the suggested responsible factors are increasing milk production, increasing herd size, confinement housing being more widespread, labor shortage and higher inbreeding percentages [2]. Various studies have shown that poorer reproduction can be due to e.g. the dairy cow’s genetic background, its nutrition and/or its physiological status [4–6] and that this especially influences the dairy cow’s ability to handle the stressful condition that it meet during the first period after calving. Improvement through breeding strategies has been hindered by the low heritabilities of the traditional fertility traits [2]. For several years progesterone in milk or blood has been recognized as a valid indicator of different cow states related to reproduction [7]. If cow level information on progesterone level is available as a routine, and especially, if automated, it might be possible to improve reproductive efficiency. Attempts at automation and inline application of quantitative enzyme immunoassays for progesterone have been described but such methods either suffered from poor sensitivity or are still too cumbersome for use in the milking parlor [8]. Biosensor technology seems to make automated inline progesterone measurement a future opportunity that needs to be considered. Biosensor for inline measurement of progesterone in milk has been developed [8–10]. These systems can provide a read-out of results within few minutes (<10). By using Ridgeway ELISA assay as reference two of these systems have provided coefficients of variation within 0–25 ng/ml of 12.5% [9] and coefficients of variation within 0–50 ng/ml of <5% [8], respectively. By using radioimmunoassay another system has provided a 95% confidence interval for a 0.5 ng/ml reading of 0.2–1.5 ng/ml [10]. These studies suggest the possibility of commercial on-farm use of automated inline indicators of progesterone in milk in near future. However, the break-even prices of an investment for such a system may differ significantly between herds. One reason is that the calculations of the herd effects of improved reproduction efficiency are complicated by significant interactions with the culling strategy in the herd [11]. Until now, studies on the economic effect of using progesterone as an indicator have been restricted to specific cow states and have used only manual test kits [12,13]. Based on the problem of low reproductive efficiency and the coming of biosensor technology there seems to be a need for methods and estimates to evaluate the potential of routinely measured progesterone information on cow level. The objectives of this study were to develop a herd simulation

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model capable of estimating the herd effects of the various features of an inline progesterone indicator and to estimate these herd effects in different herd situations.

2. Materials and methods 2.1. Model structure 2.1.1. General framework The model used was a modified version of the model called SimHerd III [14]. This is a dynamic and discrete model as it simulates the production and stage changes of the dairy herd (including young stock) by weekly time steps. All discrete events and individual variation at cow level (such as estrus detection, conception, conceptus losses, sex and viability of the calf, variation in milk yield potential, diseases, involuntary culling, and death of animals) are generated stochastically, using random numbers from relevant distributions. Different management scenarios are simulated by changing user-modifiable model input parameters. The consequences, at herd level, are simulated indirectly by parallel simulation of state changes of each individual animal in the herd. The SimHerd III model has been extended for the purpose of this study in order to obtain a more detailed representation of reproduction. In the following, the new reproductive model is described. 2.1.2. Modeling reproductive status of the cow The lactation stage is defined by week after calving, where week zero is the week of calving. The pregnancy status defines whether the cow is pregnant or nonpregnant. A cow shifts into the nonpregnancy state in cases of calving or conceptus loss (after day 14 from conception) and the cow shifts into the pregnant state when it conceives and the conceptus is not lost within 14 days after conception. The status of a pregnant cow is further specified by week of pregnancy. Nonpregnant cows are divided into the status of either acyclic or cyclic cows. Acyclic cows shift into cyclic at the first post-partum ovulation or at conceptus loss. In the weeks from calving until ovulation the cow is acyclic. To simulate the relationship between cycle number and estrus detection rate as well as conception rate, cyclic cows can be in either of the states: first, second, third or subsequent estrus cycle. Finally, to simulate variation in estrous cycle length, each estrous cycle is divided into three phases defined as: the week of estrus, 1 week after estrus and two or more weeks after estrus, respectively. 2.1.3. Modeling variation in time of first estrus To represent variation in the length of the post-partum anestrous period, time of first estrus was modeled at the cow level by drawing a random value for day in milk (DIM) when first estrus occurs from a truncated gamma distribution and by subsequently adding a number of days to represent the effect of parity and uterine infection. User-definable parameters are available for alfa, beta, min. DIM and max. DIM of the truncated gamma distribution. Further, user-definable parameters are available for subsequent adjustment of the anestrous period by a certain number of days for the effect of parity and by adding a

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certain number of days if the cow had a uterine infection, respectively. Finally, the DIM value is rounded off to the respective week after calving. 2.1.4. Modeling variation in estrous cycle length To represent variation in estrus cycle length (beyond what is related to conceptus loss), a user-definable transition probability from phase ’two or more weeks after estrus’ and ’week of estrus’ has been implemented in this study. If the transition probability is set to 1.0, the estrous cycles will always be 3 weeks long. If the transition probability is reduced to e.g. 0.90, then only 90% of the cycles will be 3 weeks long, 90% of the remaining weeks will be 4 weeks long, etc. 2.1.5. Modeling estrus detection rate and conception rate depending on cycle number Simulation of reduced estrus expression during the first estruses after calving is allowed by user-definable estrus detection rates for the cow at first, second and third estrous cycles, respectively, relative to subsequent estrous cycles. In the case of embryo loss later than 2 weeks after conception the cow is then considered to be in first phase of the first cycle. A user-definable parameter for setting a different estrus detection rate during the summer season relative to during the winter season is also available. The model for conception rate is specified by user-definable parameters for conception rate later than third cycle, and effect modifiers specified as relative conception rates for the first, second, and third oestrus cycles. Similarly, the effect of occurrence of retained placenta and uterine infection in current lactation are also user-definable. The definition of conception rate in SimHerd is related to day 14 (second week) after conception (CR14). From commercial dairy herds practicing pregnancy examination performed by palpation per rectum, the typically reported conception rate is related to day 42 after insemination (CR42). The CR42 can be calculated from the parameter value of CR14 and the parameter values for conceptus loss. The model for conceptus losses is based on an exponential distribution describing those conceptus losses that occur later than 14 days after conception. The respective conceptus loss parameters indicate a probability that a viable embryo 14 days after conception will be lost (A14) and number of days where 50% of these conceptus losses have occurred, the median (P50). The lambda parameter for the exponential distribution is equal to P50/ln(2). The ratio CR42/CR14 is equal to 1 (A14  x), where x is the proportion of A14 that has taken place 42 days after conception. The value of x is the probability from the cumulative distribution of the respective exponential distribution at 42–14 days. Mistimed AI in unpregnant cows with no chance of conception of an embryo and mistimed AIs in pregnant cows are simulated indirectly through estrus detection rate, conception rate and risk of conceptus loss. 2.1.6. Modeling effect of calving interval on subsequent lactation yield To represent the effect of previous calving interval on milk yield, the new SimHerd has been extended by supplementing the existing model with a direct effect of calving interval on milk yield. In the existing model, subsequent lactation yield was related to previous calving interval exclusively by the carry over effect of body weight. Milk yield in the SimHerd model is basically determined by energy intake, milk yield potential, and current

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body weight. Consequently, if a longer calving interval has increased the body weight at calving then the energy distribution function in the model will result in relatively more energy for milk production in the subsequent lactation. In the modified model, this indirect effect of calving interval on milk yield in subsequent lactation is supplemented by a direct effect calving interval on milk yield with separate effects for parities two and three and separate effects in 1–24 weeks after calving and >24 weeks after calving. The unit is kg ECM per day of previous calving interval and it is specified by a linear function of previous calving interval by the marginal value at 364 days of previous calving interval and the number of days of previous calving interval where the effect becomes zero. The lastmentioned parameter is defined as one value providing the intercept of all linear relationships. 2.2. Default parameter values 2.2.1. The general production and management strategy The default parameter values were intended to represent a typical loose housing system and management strategy for a 120-cow dairy herd with additional young stock. The cows were Holstein breed with an average yield capacity of 34 kg ECM during the first 24 weeks of third lactation. The standard deviation in yield capacity between animals was 2 kg and the standard deviation in yield capacity between successive lactation was 2 kg. The total risk of stillbirth and death among calves was set to be 12% and 8% for calves born by primiparous and multiparous cows, respectively. It is assumed that all cows in milk were fed ad libitum with one of three total mixed rations, differing in energy density. The first ration was fed to: all cows during the first 24 weeks after calving, to high yielding cows, and to cows on the culling list. The second ration was fed to medium yielding cows, and the third ration was fed to low yielding cows. The classification for cows being high yielding, medium yielding and low yielding was based on the current daily milk production and is specified for primiparous and multiparous cows, respectively. Dry cows were fed restrictively the ration whereas other cows were fed ad libitum. The energy density of the rations resulted in a net energy intake of 20.0, 16.1 and 13.5 Scandinavian feed units (SFU) for a third parity cow 3 months after calving with an average feed intake capacity. One SFU equals 7.89 MJ net energy for lactation. Nonpregnant cows producing less than 10 kg milk per day were culled immediately. Involuntary culling was defined as a constant risk of 0.0033 per week for all cows. This sums to a risk of 0.18–0.20 per cow-year, depending on the management strategy. Voluntary culling was based on whether the cow became pregnant within the AI period. If the AI period terminated without a positive pregnancy test the corresponding cow was designated for voluntary culling (i.e. put on the culling list). Forty-two days after last AI a pregnancy test result was simulated. If a positive pregnancy test result appeared subsequent to the AI period, the designation for voluntary culling was cancelled. The replacement of the particular cow on the culling list took place when that cow was the lowest yielding among the cows on the culling list and the next pregnant heifer was due to calve. The length of the AI period, and hence designation for voluntary culling of a cow, was specified according to milk yield in the current lactation. Cows with a milk yield higher or lower than

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the parity-specific herd-average were specified to have an AI period of 224 or 182 days, respectively. These values were chosen to give a total replacement percentage in the default herd of 40%. The AI periods were initiated 35 days after calving and cows were dried off 7 weeks before calving. 2.2.2. Default values of new model parameters Based on progesterone measurements taken three times a week, commencement of first luteal phase has been found to be right skewed with mode of 16–20 days post-partum, mean of 29.4 days and an S.D. of 18.4 days [15]. In a study of risk factors for duration of postpartum anestrous period geometric means of +1.5 days per 10 kg of genetic merit fat, +5 days for uterine infection, and a seasonal effect of +1 week for winter and spring relative to summer and autumn have been found [16]. Significantly longer durations were also found for parity 4+, whereas no significant effect was found for herd, year, retained placenta, dystocia and diet [16]. Based on these results, together with the results from a meta analysis of the effect of diseases [17], and previous applied parameters in simulation modeling [13] the default values for the truncated gamma distribution to describe time of first estrus (days after calving) were set to: alfa = 2.42, beta = 11.6, min. truncation = 14, max. truncation = 98, and days extra due to metritis = 7. An indirect effect on the duration of post-partum anestrous period is represented by a significant effect of retained placenta on the risk of metritis (odds ratio = 5.0) [14]. The parity number above which cows are defined as old was parity 4 and days extra due to being old were 7 days. The resulting number of days is converted in the model to the equivalent number of weeks. The resulting distribution for cows (
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15% and 7%, respectively [20]. From these numbers the embryo loss rate of viable embryos at day 14 can be calculated to be 13% ((0.15 + 0.07)  0.40/(1 0.78  0.40)). Furthermore, the conception rate to day 14 and day 42 can be calculated to be 0.62 (0.90  (1 0.40  0.78)) and 0.57 (0.90  (1 0.40  (0.78 + 0.15))), respectively. The rate and profile from the review study [20] of conceptus losses were simulated by a conceptus loss rate (A14) of 0.13 and a parameter value (P50) of 17 for days by which 50% of losses of conceptus have occurred. The current typical Danish conception rate (day 42) is around 0.35–0.40 (range 0.20–0.65). The discrepancy between this number and the one of 0.57 is assumed to be explained by sub-optimal timing of AI including mistimed AIs. We simulated this indirectly by a default conception rate (CR14) of 0.40. With the applied rate and profile of conceptus losses the CR42 then becomes 0.36 in cycle 3+ in cows with no reproductive disorders. The cost of performing the mistimed AIs was calculated on the basis of the actual simulated number of AIs as explained in the subsequent section on the assumed effect of a progesterone indicator. The standard values for the effects of retained placenta and metritis on conception rate were based on an epidemiological study [21]. Accordingly, the relative conception rate for both conditions was simulated to be 0.85. The period of this effect was set to be 17 weeks. A cow exposed to both retained placenta and metritis was assumed to have a relative conception rate of 0.72 (0.85  0.85). The effect of cycle number on conception rate was simulated by relative conception rates of 0.95 and 1.00 for first and second cycles, respectively. These effects on conception rate are relative to the default conception rate (CR14) for cows in cycle 3+ with no reproductive disorders. The effect of calving interval on milk yield in the subsequent lactation has recently been studied with data from Israel [22] and Denmark [23], respectively. The Israeli study [22] was based on planned postponed AI whereas the Danish study [23] was based on data from the National Cattle Database. The Israeli study found a 1.7 kg milk increase from 189 days open compared to 133 days open [22]. This is equivalent to 0.028 kg per day extra. A nonsignificant total effect of 0.6 kg was estimated for multiparous cows [22]. The Danish study found an effect of 0.017 and 0.012 kg per day from extending first and later calving intervals, respectively [23]. The effect was independent of milk yield level and the calving interval to which the one-day was added. For this study, default values of 0.017 and 0.012 for extending first and later lactations, respectively, were chosen as the marginal effect at 364 days calving interval. The effect was assumed to decline to zero at 700 days calving interval. No reduction was assumed in the effect in the second part, compared to first part, of the subsequent lactation. As an example, a parity 2 cow will yield 1.0 kg EKM more per day if the previous calving interval was extended from 357 to 420 days. In addition to the direct effect of extended lactation, increased body weight at calving achieved as a consequence of extended period of body weight gain provides an additional but much smaller indirect effect on (increased) feed energy allocated towards milk yield in the subsequent lactation in the model. 2.2.3. Effects of progesterone monitoring The quality of information about certain reproductive parameters may be improved by using progesterone monitoring. In the simulation model, the effects of progesterone monitoring on estrus detection, length of the post-partum anestrous period and number of

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mistimed AIs were considered. Other potential effects such as: identification of abnormal cycles, typing of cysts, testing for pregnancy and conceptus loss, prediction of conception date (and subsequent calving date) in case of unknown breeding date (e.g. bull breeding), indication of energy balance, indication of pregnancy chance at current estrus, etc., were not included in the simulations. 2.2.3.1. Estrus and AI. Progesterone can be used to identify the start of the follicular phase, typically at days 17–19 of the estrous cycle, where progesterone is low indicating that ovulation and estrus will occur with a high probability. This information can be used (1) to focus the traditional estrus detection in these follicular periods or (2) it can be used to release AI without further information. In the latter case it will be relevant to provide AI either once at a fixed time after the drop in progesterone concentration or combining it with an extra AI (double AI) if progesterone is still low 1 or 2 days later (in principle similar to providing AI again on a subsequent day if the cow is still showing visual estrous behavior). Results have been presented showing that the occurrence of an estrus event on the days 1, 2, 3, 4, 5, 6, and 7 following a fall to a low progesterone level (<5 ng/ml milk) was 4%, 32%, 63%, 35%, 18%, 6% and 2%, respectively [24]. Progesterone may become low, not only because of estrus but also because of a follicular cyst. However, with regular progesterone measurements, this will typically be detected by a prolonged period of low progesterone. A special feature of estrus detection according to progesterone is that the first estrus after calving cannot be identified for AI by progesterone alone because progesterone is low until the first ovulation has occurred. Silent heat (ovulation accompanied by very weak estrous expression) is rarely detected by visual estrus detection. These ovulations are expected to be detected frequently, using an inline progesterone indicator. Silent heat has been found to account for as many as 40% of progesterone detected estruses [25]. However, the benefit from the progesterone indicator, for providing AI at silent heat, may be low since the conception rate from such AIs has been shown to be low (30%) [26]. In, general, the use of progesterone tests for estrus detection has been found to exhibit a very high sensitivity (above 90%) and a relatively low error rate [26–28]. However, it should be noticed that the gold standard of true estrus in these studies is based on laboratory analysis of progesterone level. As another measure of the efficiency of a progesterone indicator it has been estimated that median inter-service interval was reduced by 7 days [26]. Based on these findings, an implementation of a progesterone indicator resulting in an estrus detection rate of 90% and another implementation resulting in an estrus detection rate of 70% were simulated in this study. The two scenarios could represent differences in progesterone sampling frequency, presence or absence of visual estrus detection and the use of single versus double AI. In traditional reproductive management mistimed AI may account for 5–46% of AIs [29,30]. It has bee found that there are no biologically negative effects of mistimed AIs during luteal phase [30]. When using an inline progesterone indicator these mistimed AIs can be eliminated resulting in proportionally reduced AI costs. If mistimed AI is performed in a pregnant cow it may cause loss of the conceptus. A 24% embryo loss in reinseminated pregnant cows has been found, which was higher than a 7% spontaneously embryo loss in

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the corresponding control cows that were not reinseminated [30]. Carrying out reinseminations only when estrus is confirmed by a progesterone kit or with the insemination pipette only about half way through the cervix may reduce the risk of conceptus loss due to mistimed AI. However, this latter technique may reduce the conception rate by 5–10% [31]. The inline progesterone indicator could be used to ensure no such reinseminations in pregnant cows or the drawbacks from the alternative AI technique [30]. From the commercial herds in Israel during 1996 and 1997 it was found that trained inseminators did correctly reject 95% of pregnant cows submitted for AI [30]. The group of rejected AIs in pregnant cows represented 7% of all cows submitted for AI. Based on these results and by assuming up to 2.5 AIs per lactation, the corresponding risk of conceptus loss per lactation becomes 0.15% (2.5  0.07  0.05  (0.24 0.07)). Further, the potential benefit from an inline progesterone indicator for avoiding mistimed AI during luteal phase and in pregnant cows was assumed to be due mainly to reduced costs for inseminator and semen. This was included in the present study, by assuming 10% mistimed AIs in scenarios without a progesterone indicator and 0% with a progesterone indicator. The only effect of these mistimed AIs is assumed to be the cost of performing them. 2.2.3.2. Duration of post-partum anestrous period. The first progesterone rise (>3 ng/ml in whole milk) after calving indicates the end of first follicular phase and thereby resumption of ovarian activity. Consistently low progesterone levels for the first 50 days after calving has been applied as a definition of delayed cyclicity [31–33]. One study estimated an incidence of 20.5% of which 95% were related to inactive ovaries and the remaining 5% were caused by ovarian cysts [32]. In a review [25], it was found that the incidence of anestrus around 50–60 days after calving varied from 2% to 10% between different studies. Cows with inactive ovaries can sometimes be induced to ovulate by PRID treatment, however, the treatment is often unsuccessful [25,31]. A reduction in interval from time of treatment to conception of no more than 7 days has been reported [34]. Underlying disease states are often looked for rather than treatment [25]. In a herd with a high frequency of cows with delayed cyclicity, it may be more relevant to address the underlying risk factors. Based on these findings, the effect of an inline progesterone indicator, in relation to the duration of the post-partum anestrous period, is simulated by assuming that it result in a maximal period of 60 days. This reduced anoestrus period with the progesterone indicator is assumed to be due to cow- or herd management that reduces the number of cows with delayed cyclicity. However, the method by which the reduction is achieved is not specified and therefore no specific costs are assumed. 2.3. Simulation scenarios The base situation for the simulations in this study was a default herd without a progesterone indicator and estrus detection based on visual observation. Any improvement in estrus detection, all other things being equal, will cause a reduced herd replacement rate. To evaluate the importance of the effect on replacement, each of the two implementations of a progesterone indicator was simulated at three levels of voluntary culling. This was accomplished by simulating each progesterone indicator scenario with an unchanged and two shortened AI periods, respectively. The parameters for ending the AI period in the

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Table 1 Input parameter values that define the seven scenarios for the default herd with two different implementations of an inline progesterone indicator and with varying voluntary culling rates implemented through different durations of the AI period Default herd

Progesterone implementation 90% AI period Unchanged

Max. anestrus (days) Estrus detection rate Cycle 4+, Cycle 1, relative ratea Cycle 2, relative ratea Cycle 3, relative ratea AI period Duration high yieldersb (days) Duration low yieldersb (days) a b

98 0.50 0.40 0.80 0.95

60 0.90 0.22 0.95 1.00

Progesterone implementation 70% AI period

Red. 42 60 0.90 0.22 0.95 1.00

Red. 84 60 0.90 0.22 0.95 1.00

Unchanged 60 0.70 0.29 0.95 1.00

Red. 42 60 0.70 0.29 0.95 1.00

Red. 84 60 0.70 0.29 0.95 1.00

224

224

182

140

224

182

140

182

182

140

98

182

140

98

Estrus detection rate relative to estrus detection rate in cycle 4+. High and low yielders are defined according to parity-specific herd average milk yield.

scenarios without the progesterone indicator were found by test simulation to be at the economic optimal level or non-significantly different from that. Table 1 summarizes the input parameter values that define the seven scenarios: default plus the three AI periods at each of the two progesterone implementation levels. These seven scenarios were replicated three times to analyze the importance of implementing a progesterone indicator in different herd situations. Firstly, a herd with poor reproduction efficiency was analyzed by simulating an estrus detection rate (cycle 4+) of 40% and a conception rate (CR14) of 35%. To adapt the poor reproductive efficiency in this herd the AI period was extended by 70 days. Secondly, a herd with high reproductive efficiency was simulated by an estrus detection rate (cycle 4+) of 70% and a conception rate (CR14) of 62%. Thirdly, a herd using a 9-week postponed AI period for primiparous cows was simulated with the default estrus detection rate (cycle 4+) of 50% and the conception rate (CR14) of 40%. 2.4. The simulation procedure A simulation with the modified SimHerd III model is a forecast for an initial herd controlled by a set of parameter values. The initial herd is the state of the individual animals in the herd at the beginning of simulation. Through test simulations, we have previously found that after simulations of 5 years, the effect of the initial herd was diminished. To address the effect of the scenarios (production system and management strategy defined by the parameter values) rather than the effect of the initial herd and to avoid having to analyze repeated measures, we used average annual results from the last 5 years of each replicate. Each simulated scenario was replicated 500 times.

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2.5. Applied prices and costs The economic consequences of the implementations of an inline progesterone indicator in the four different herd situations were studied by applying a set of assumed 2004/2005 Danish prices and costs for the different technical results (see Table 2). Herd profit was calculated as sales income less variable costs (feed, AIs, veterinary assistance, medicine

Table 2 Prices and costs in s (s1 = 7.45 Danish Kroner) Factor

Unit

Milk

s/kg ECMa

Livestock Slaughter cows Slaughter heifers Bull calves Pregnant heifers Dead cow

s/kg body weight s s s s

Feed cows Mix 1 Mix 2 Mix 3

s/SFUb s/SFU s/SFU

0.17 0.16 0.15

Feed heifer Milk replacer for calves Concentrates for calves Roughage for calves

s/kg powder s/SFU s/SFU

1.61 0.16 0.13

Grazing heifers First year Second year

s/day s/day

0.13 0.27

Veterinary costsc Milk fever Dystocia Downer (destruction) Retained placenta Metritis Left displaced abomasum Ketosis Mastitis

s/case s/case s/case s/case s/case s/case s/case s/case

Other costs Cowsd Heifersd AI Interest rate of the herd value

s/cow/year s/heifer/year s %

a b c d

etc.

Value 0.303 0.940 456 161 940 18

96 107 27 38 44 89 44 70 40 13 16 6

ECM = energy-corrected milk. SFU = Scandinavian feed unit. One SFU is equivalent to 7.89 MJ net energy for lactation. Covering also retreatment costs within current lactation. Covering average costs for bedding, milk recording, pregnancy test, additional veterinary assistance and drugs,

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and other costs) for cows and heifers. Labor and management costs were not included as variable expenses.

3. Results 3.1. Technical results Technical results of the different implementations of the progesterone indicator are summarized for the default herd in Table 3. By varying the AI period for all combinations Table 3 Annual mean technical effects simulated by SimHerd for scenarios in the default herd and relative mean effects of the different implementations of a progesterone indicator in this herd Output variable

Without progesterone implementation

Progesterone implementation 90%a

Progesterone implementation 70%

Mean

AI period

AI period

S.D.

Unchanged Red. 42 Cow-years Heifer-years Replacement percentage Calvings per cow-year Calves deadb per calves born Weight per slaughter cow (kg) Dead cows per cow-year AIs of cows per cow-year Net sale of pregnant heifers per cow-year Feed intake, SFUc per cow-year Milk production, kg ECMd per cow-year Disease cases per cow-year Milk fever Downer Dystocia Retained placenta Metritis Displaced abomasums Ketosis Mastitis a b c d

118.5 138.3 39.8 1.11 10.7

0.22 6.71 1.91 0.018 1.24

601

9.8

0

0.67 0.069 0.022

0.3 0.22 0.13

2.8 2.71 0.06

0.3 2.5 11.8 0.02 0.3

0.2 8.9 5.5 0.07 0.1 13 0.1 0.23 0.09

Red. 84

Unchanged Red. 42

0.0 20.7 7.1 0.17 0.2

0.2 1.8 7.3 0.01 0.1

36

3

0.0 0.20 0.00

0.2 0.06 0.08

0.1 9.3 0.5 0.07 0.1 16 0.1 0.04 0.03

Red. 84 0.2 11.3 8.8 0.09 0.2 1 0.1 0.22 0.05

5642

19

1

25

45

7

29

85

8616

49

19

66

113

9

70

70

0.05 0.03 0.01 0.07 0.03 0.01 0.05 0.25

0.010 0.003 0.007 0.011 0.007 0.005 0.010 0.020

0.03 0.01 0.00 0.01 0.00 0.00 0.02 0.02

0.01 0.00 0.00 0.01 0.00 0.00 0.01 0.02

0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.04

See Table 1 for definition of scenarios of progesterone implementations. Include stillbirth and death of young stock. SFU = Scandinavian feed unit. ECM = energy-corrected milk.

0.02 0.00 0.00 0.01 0.00 0.00 0.01 0.01

0.00 0.00 0.00 0.01 0.00 0.00 0.01 0.02

0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01

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of herds and progesterone implementations, simulation results were derived for a range of replacement percentages that encompasses the respective scenario without a progesterone indicator. One exception though appeared for the progesterone implementation with 90% estrus detection rate in the herd with poor reproductive efficiency where the replacement percentages were lower than in the respective scenario without a progesterone indicator. From the scenarios with implementations of the progesterone indicator where the replacement percentage is closest to the respective scenario without progesterone indicator, it appeared (especially in the herd with poor reproductive efficiency) that milk yield per cow-year was higher and that the cows were slaughtered at a significantly lower body weight. The latter indicates slaughtering at an earlier stage of lactation. This effect of the progesterone indicator is a consequence of a reduced shortage of pregnant heifers. The economic results from implementing the inline progesterone indicator and keeping the AI period unchanged were generally superior to the scenarios with reduced AI periods. Therefore, scenarios with reduced AI periods are only presented for the default herd (Table 3). Standard deviations of each output variable were similar across scenarios, and therefore they also were only included for the default herd. Differences in disease cases were also small, and consequently these results are also presented only for the default herd. With these reductions, the technical results of the different implementations of the progesterone indicator are summarized in Tables 4–6 for the herd with poor reproduction efficiency, the herd with high estrus detection rate and the herd using a 9-week postponed AI period for primiparous cows, respectively. From the technical results in Tables 3–6, it appears that the replacement percentage decreases when implementing the inline progesterone indicator and keeping the AI Table 4 Annual mean technical effects simulated by SimHerd for scenarios in the herd with poor reproduction efficiency and relative mean effects of the two implementations of a progesterone indicator with unchanged AI period Output variable Cow-years Heifer-years Replacement percentage Calvings per cow-year Calves deadb per calves born Weight per slaughter cow (kg) Dead cows per cow-year AIs of cows per cow-year Net sale of pregnant heifers per cow-year Feed intake, SFUc per cow-year Milk production, kg ECMd per cow-year a b c d

No progesterone implementation 117.8 123.9 37.2 0.99 10.8 634 2.7 2.65 0.03 5577 8401

Progesterone implementation 90%a 0.9 10.8 12.7 0.08 0.5 26 0.4 0.58 0.16 26 155

See Table 1 for definition of scenarios of progesterone implementations. Include stillbirth and death of young stock. SFU = Scandinavian feed unit. ECM = energy-corrected milk.

Progesterone implementation 70% 0.8 8.7 9.6 0.06 0.3 25 0.3 0.38 0.12 6 122

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Table 5 Annual mean technical effects simulated by SimHerd for scenarios in the herd with high reproductive efficiency and relative mean effects of the two implementations of a progesterone indicator with unchanged AI period Output variable Cow-years Heifer-years Replacement percentage Calvings per cow-year Calves deadb per calves born Weight per slaughter cow (kg) Dead cows per cow-year AIs of cows per cow-year Net sale of pregnant heifers per cow-year Feed intake, SFUc per cow-year Milk production, kg ECMd per cow-year a b c d

No progesterone implementation 118.9 139.1 25.1 1.14 10.4 601 3.1 2.19 0.25 5647 8629

Progesterone implementation 90%a 0.1 2.0 2.1 0.02 0.1 2 0.1 0.14 0.03 15 14

Progesterone implementation 70% 0.0 0.4 0.1 0.01 0.1 0 0.1 0.20 0.00 2 1

See Table 1 for definition of scenarios of progesterone implementations. Include stillbirth and death of young stock. SFU = Scandinavian feed unit. ECM = energy-corrected milk.

period unchanged. The largest effects ( 12.7%-units and 9.6%-units) are found in the herd with poor reproduction efficiency and the smallest effects ( 2.1%-units and 3.8%units) are found in the herd with a high estrus detection rate. In the herd with poor reproductive efficiency it appeared that the number of AIs in cows increased Table 6 Annual mean technical effects simulated by SimHerd for scenarios in the herd using a 9-week postponed AI period for primiparous cows and relative mean effects of the two implementations of a progesterone indicator with unchanged AI period Output variable Cow-years Heifer-years Replacement percentage Calvings per cow-year Calves deadb per calves born Weight per slaughter cow (kg) Dead cows per cow-year AIs of cows per cow-year Net sale of pregnant heifers per cow-year Feed intake, SFUc per cow-year Milk production, kg ECMd per cow-year a b c d

No progesterone implementation 118.4 130.6 37.7 1.04 10.7 609 2.8 2.53 0.05 5621 8588

Progesterone implementation 90%a 0.3 4.7 10.8 0.04 0.3 6 0.2 0.26 0.12 13 57

See Table 1 for definition of scenarios of progesterone implementations. Include stillbirth and death of young stock. SFU = Scandinavian feed unit. ECM = energy-corrected milk.

Progesterone implementation 70% 0.2 3.1 6.5 0.02 0.2 8 0.1 0.10 0.08 1 38

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significantly (Table 4), whereas in the herd with high estrus detection rate the number of AIs in cows decreased (Table 5). This decrease was partly a result of fewer mistimed AIs. Generally, milk production per cow-year increased when using a progesterone indicator, however, the effect was significant only in the herd with poor reproductive efficiency. 3.2. Economic results The economic results of the different implementations of the progesterone indicator are summarized in Tables 7–10 for the default herd, the herd with poor reproduction efficiency, the herd with high estrus detection rate and the herd using a 9-week postponed AI period for primiparous cows, respectively. In all combinations of herds and progesterone implementations the economic effect of implementing the progesterone indicator when keeping the AI period unchanged compared favorably to the scenarios in which the AI period was reduced. Consequently, as for the technical results, the presentation of the economic results is focused on the scenarios with unchanged AI Table 7 Annual economic results in s for scenarios in the default herd and effects of the different implementations of a progesterone indicator in this herd Results

No progesterone implementation

Progesterone implementation 90%a

Progesterone implementation 70%

Mean

AI period

AI period

S.D.

Unchanged Annual income per cow-year Milk production 2614 Slaughter and 208 dead cows Heifers 73 Bull calves 80 Balance 0 displacement Total 2974

Red. 42

Red. 84

Unchanged

Red. 42

Red. 84

15 10

6 69

20 36

34 25

3 43

21 3

21 49

21 4 12

118 2 0

81 5 1

2 12 1

75 1 0

25 6 0

48 7 2

25

57

70

70

35

49

30

4 9

3 3

5 12

13 29

3 3

7 13

22 16

Annual expenses per cow-year Feeds for cows 957 Feeds for young 192 stock Other costs cows 117 Other costs heifers 37 Interest of the 84 herd value Total 1386

2 2 2

10 1 1

8 2 2

7 6 6

4 1 0

3 3 3

3 3 3

14

12

30

60

5

29

41

Annual profit Per cow-year Per 1000 kg ECM

17 1

46 5

39 3

10 1

31 3

20 1

11 3

a

1588 184

See Table 1 for definition of scenarios of progesterone implementations.

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Table 8 Annual economic results in s for scenarios in the herd with poor reproduction efficiency and effects of the two implementations of a progesterone indicator with unchanged AI period No progesterone implementation

Progesterone implementation 90%a

Progesterone implementation 70%

Annual income per cow-year Milk production Slaughter and dead cows Heifers Bull calves Balance displacement Total

2549 205 45 72 2 2869

47 84 150 6 2 121

37 65 114 4 2 91

Annual expense per cow-year Feeds for cows Feeds for young stock Other costs cows Other costs heifers Interest of the herd value Total

942 173 112 35 80 1342

2 14 18 3 3 40

1 11 13 2 2 27

Annual profit Per cow-year Per 1000 kg ECM

1527 182

81 6

64 5

Results

a

See Table 1 for definition of scenarios of progesterone implementations.

Table 9 Annual economic results in s for scenarios in the herd with high reproductive efficiency and effects of the two implementations of a progesterone indicator with unchanged AI period Results

No progesterone implementation

Progesterone implementation 90%a

Progesterone implementation 70%

Annual income per cow-year Milk production Slaughter and dead cows Heifers Bull calves Balance displacement Total

2618 124 239 82 0 3061

4 13 29 1 2 20

0 1 2 1 0 2

Annual expense per cow-year Feeds for cows Feeds for young stock Other costs cows Other costs heifers Interest of the herd value Total

955 192 117 31 84 1378

3 3 0 0 1 6

1 0 3 0 0 2

Annual profit Per cow-year Per 1000 kg ECM

1683 195

13 1

3 0

a

See Table 1 for definition of scenarios of progesterone implementations.

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Table 10 Annual economic results in s for scenarios in the herd using a 9-week postponed AI period for primiparous cows and effects of the two implementations of a progesterone indicator with unchanged AI period Results

Progesterone implementation 70%

No progesterone implementation

Progesterone implementation 90%a

Annual income per cow-year Milk production Slaughter and dead cows Heifers Bull calves Balance displacement Total

2605 199 68 75 0 2947

17 65 116 3 0 72

12 40 71 2 1 44

Annual expense per cow-year Feeds for cows Feeds for young stock Other costs cows Other costs heifers Interest of the herd value Total

952 181 112 35 82 1362

0 6 11 1 1 20

1 4 5 1 1 9

Annual profit Per cow-year Per 1000 kg ECM

1585 185

53 5

35 3

a

See Table 1 for definition of scenarios of progesterone implementations.

period. Results from the reduced AI period are included only for the default herd (Table 7). The economic results per cow-year showed an increased herd profit of the order of s3– 81 per cow-year over the range of different implementations of a progesterone indicator with unchanged AI periods. This is equivalent to 0.2–5.2% of the herd profit per cow-year. When analyzing the herd profit per kg ECM produced, the effects were smaller, of the order of 0.2–3.4% of the profit. The economic herd effects indicate the break-even prices of an investment (before considering labor cost), which are equal to the assumed benefit of a progesterone indicator. The two measures of profit—profit per cow or profit per kg ECM produced—are relevant for evaluating situations in which the barn capacity or a milk quota, respectively, is limiting production. The largest economic effect of the progesterone implementation was found in the herd with poor reproductive efficiency and the smallest effect was found in the herd with high estrus detection rate. The entry of highest influence on the effects of a progesterone indicator was the increased income from sold pregnant heifers. The entry of second and third highest influence was the decreased income from slaughter cows and increased income from milk sale, respectively. The progesterone implementation with the 70% estrus detection rate provided approximately 2/3 of the increased herd profit found with the implementation of the 90% estrus detection rate. This was the case in the default herd and in the herd with a 9-week postponed AI period for primiparous cows. In the herd with poor reproduction efficiency and in the herd with high estrus detection rate these profit ratios were 4/5 and 1/4, respectively.

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4. Discussion The assumed effects of an inline progesterone indicator improved both the technical and economic performance of the simulated herds. The figures reported here are comparable with previous published results [12,13], though direct comparisons are difficult because of differences in technical and economic assumptions. One study estimated a US$ 13 increased net return per cow-year from using (one, two or three) progesterone test kits for the determination of nonpregnant cows around 21 days after AI, whereas using it to prevent mistimed inseminations was found to be unprofitable [12]. Using a progesterone test kit for the determination of nonpregnant cows around 21 days after AI was assumed to diagnose pregnancy (saved cost of examination by palpation per rectum at veterinary visit 45 days post-service) as well as to initiate treatment with prostaglandin in cases of continued low progesterone level and no observed estruses in that study. In another study an increased net return per cow-year of US$ 11 was found from using progesterone kit testing once a week from 30 days after calving to monitor return to cyclicity whereas the use of the test to classify follicular and luteal cysts and to select the appropriate therapy was not found to be economically justifiable [13]. It was assumed that the schedule for monitoring for return to cyclicity would increase estrus detection rate to 75% and that more cows would be treated with prostaglandin [13]. A cost of US$ 4.50/sample was assumed for the progesterone test kit in both studies [12,13]. Information on the effect on net returns excluding the cost of the progesterone tests was not supplied by these studies. However, if three samples were used per cow-year for the two profitable applications of the progesterone kit then the positive effect becomes 11 + 13 + 3  4.5 = US$ 37.5 per cow-year, which is equivalent to the result from the default herd in our study. Both in our study and in the other two studies [12,13], an interaction with the reproductive efficiency in the herd was found. The finding that the herd with poorer reproductive efficiency benefits more from tools for improved reproduction has also been found in studies on timed AI protocols [35,36]. The different technical and economic effects of a progesterone indicator in different herds suggest that generalizing the results to herds that differ from the ones simulated here should be done with caution. However, since reproductive efficiency and the use of stage of lactation to initiate the AI period are expected to be the most influential herd reproduction characteristics, it is expected that the presented range of effects covers the majority of herds. As seen in the simulated herd with poor reproductive efficiency, shortage of pregnant heifers may reduce herd performance. Consequently, in a herd with a consistent shortage of home-grown pregnant heifers the benefit from a progesterone indicator would increase. The price of pregnant heifers and the risk of introducing infection diseases from other herds then become important factors. The significant interaction found between herd type and type of progesterone implementation indicates that different herds might aim for different sampling frequencies or precision of the indicator depending on the cost of the indicator. For example, a herd with a poor reproductive efficiency might choose an implementation like the simulated progesterone implementation of 70% because this strategy would achieve 4/5 of the expected benefit of the implementation with 90% estrus detection rate. In most cases the effect of an inline progesterone indicator will be related to increased estrus detection. A recent review of other automatic estrus detection systems has been

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provided [37]. The scenarios in the present study of the herd with a heat detection rate of 70% might be interpreted as a herd with such an alternative estrus detection system. Consequently, the economic effects found in this herd can be interpreted as an indicative break-even cost that can be paid for the progesterone implementation relative to the cost of other automatic estrus detection system. The model developed and used in this study offers the opportunity of analyzing herd effects of alternative systems under various herd situations. Herd simulation modeling is recognized as a relevant method for studying the complex herd effects of alternative reproduction management [11]. The simulation model used for this study was based on a previous documented model [14] and extended in this study by a new module with a more detailed description of reproduction. The new module was validated by means of sensitivity analysis and face validation. However, the results provided are dependent on the assumptions and parameter values described in the material and methods. The parameters assumed for this study ignore the uncertainties in their determination. A thorough sensitivity analysis including all parameters involved would not be feasible. However, the uncertainty related to the effect of an inline progesterone indicator on the estrus detection rate is probably the most influential uncertainty. The results from the two different progesterone implementations differ specifically on the estrus detection rate and, consequently, these results indicate the importance of that uncertainty. Another uncertainty, which can be evaluated from the simulated results, is the consequences if the progesterone indicator were able to improve the conception rate in the herd. The general assumption in this study was that the conception rate would remain unchanged when the progesterone indicator was implemented. However, if the conception rate was increased from the default value of 0.40 (CR14) to 0.62 as simulated for the herd with a high reproductive efficiency, then the economic effect of the progesterone indicator would increase from s46 to 109 per cow-year in the default herd. In general, for this study we have included defendable major effects only, resulting in economic estimates, which are therefore conservative. However, there are a number of possible further benefits, particularly if the inline progesterone indicator is incorporated into a biological inference model (e.g. [38]). Some of the possible benefits are discussed in the remaining discussion. The reported economic results did not include saved labor costs when using an inline progesterone indicator. Such effects may be important but are very difficult to assess [36]. One possible interpretation of the 70% progesterone implementation as compared to the 90% progesterone implementation is that the time spent on visual estrus detection was saved and thereby also the corresponding labor cost. In a study of estrus detection systems it was found that a system of two times 30 min visual estrus detection resulted in an estrus detection rate of 0.47 [19]. It was concluded that this method might be too complicated to introduce to normal herd management, because in daily practice it is too demanding to watch cows twice a day for 30 min, especially if the cows show only vague and infrequent signs of estrus [19]. It also appeared to be too complicated to watch the herd at the most appropriate times [19]. However, it was found that if the applied estrus behavior scoring system was included in the daily routine, meaning that farmers are trained to watch for other signs than standing heat only and are able to recognize their different values, it can be a valuable aid in estrus detection [19]. Based on the findings that efficient estrus detection

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might be achieved within existing routines, it seems questionable to assume that the implementation of a progesterone indicator can save considerable labor time by specifically reduced time spent for visual estrus detection. Further, if a reduced observation time was assumed it might also cause negative effects on health and welfare monitoring. However, if the progesterone indicator could save half an hour of labor daily per 100 cowyears with a cost of s20 per labor hour and without any negative side effects, then the economic effects per cow-year becomes s37 higher in terms of profit after labor costs. The economic effect found in this study did not include costs related to extra AI if the information on progesterone level is assumed to be used for double AIs, in cases where the progesterone level does not raise the first day or 2 days after AI. Applying this practice might be relevant but it would require very frequent progesterone measurements and a very high accuracy of progesterone determination and the second AI would likely still be associated with a lower conception rate. Such double AIs would modify the results presented in this study to make up a slightly smaller economic effect of the progesterone indicator implementations. The assumed effects of a progesterone indicator did not include the saved costs of pregnancy examinations by palpation per rectum and a veterinary reproductive program. From a review of on-farm milk progesterone tests, it was found that as a method of pregnancy diagnosis, milk progesterone concentration at days 21–24 post-AI was 67–88% accurate (sensitivity) for diagnosing cows subsequently determined to be pregnant and 91–100% accurate (specificity) for cows determined to be not pregnant [7]. More recently it has been stated that approximately 80–85% of positive diagnoses between 21 and 24 days after AI appear to be accurate, whereas a non-pregnancy diagnosis is almost 100% accurate [31]. Ultrasonography provides an alternative method of early pregnancy tests, which can diagnose pregnancy as early as 26 days after AI (sensitivity and specificity between 26 and 33 days after AI has been estimated to be 97.7% and 87.7%, respectively) [39]. It has been stated that it is not possible to distinguish a previously tested but non-pregnant cow with a luteal cyst from a cow that is pregnant, by means of progesterone alone [40]. A substantial number of conceptus losses occur after 24 days [31] and some even occur later than 42 days after AI. For that reason the limited pregnancy diagnosis needs to be followed up by subsequent tests for confirmation of the pregnancy at a later time after AI. In this respect, the progesterone indicator can be used to obtain an early (21 days after AI) pregnancy diagnosis with some uncertainty and to monitor conceptus losses during pregnancy. The value of getting an early pregnancy test may be related to some saved pregnancy examinations by palpation per rectum and also the possibility of meeting a rapid reinsemination i.e. at day 21 rather than day 42. In the current study this was not accounted for by specific saved expenses. The current price of having the inseminator carry out a pregnancy test by palpation per rectum is s2, hence using one or two tests per cow-year, would amount to s2–3 per cow-year. Performing the examination by palpation per rectum also in the case of a progesterone indicator might identify some reproductive problems that would not otherwise be identified until later. However, if it is assumed that an inline progesterone indicator would provide a pregnancy test with a quality similar to the pregnancy test by palpation per rectum (all other things being equal) then s2–3 per cowyear should be added to the economic effects estimated in this study. However, as it is only on approximately 5 days out of an estrous cycle of 21 days, that a progesterone indicator

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would be able to test for pregnancy, a considerable number of progesterone tests would be necessary to test for pregnancy. Various veterinary reproductive programs exist. With an inline progesterone indicator some savings might be possible. However, as reproduction is typically only part of a veterinary health program, it is complex to predict the various effects of saving some items related to reproduction, and such items were not included in our study. The economic effect found in this study did not include the effect of an inline progesterone indicator on earlier detection of conceptus losses. A progesterone indicator has no relevance for those early embryonic losses (until app. 14 days after conception) where subsequent estrus occurs after a regular 3-week period. If a pregnancy test is performed at app. 42 days after AI, there will be no benefit from using progesterone to detect conceptus losses occurring before this point of time, because the cow will be monitored for estrus anyway. However, a progesterone indicator can be used to detect conceptus losses occurring later than the time of pregnancy examination by palpation per rectum, in order to get these cows earlier back to breeding, or it can be used to decide to cull the cow and then manage it as a culling candidate. In a review it was found that app. 30% of fetal losses (including abortions) are usually observed [41]. This percentage is expected to be higher when evaluating only the abortions. Further, cows showing estrous behavior when returning to cyclicity will reveal a number of the previously undetected conceptus losses. Based on these findings and the concern that a considerable number of extra samples by the progesterone indicator would be necessary at the respective stage of pregnancy, the effect of an inline progesterone indicator in relation to detection of conceptus losses is considered to be non-significant and consequently is not accounted for in this study. The assumed effects of a progesterone indicator did not include benefits from differentiation between normal versus abnormal cycles. Prolonged luteal phase accounts for the majority of abnormal cycles [32]. Prolonged luteal phase can be defined as: ‘‘Progesterone levels remain elevated for >20 days, without a preceding AI’’ [32]. It has been found that 20% of the animals suffered from this condition and based on examination by palpation per rectum these were related to abnormal uterine content (48%), ovarian cyst (2%), and no detected abnormalities (49%) [32]. Based on data from the UK an incidence of 16% was estimated for the period from 1995 to 1998 [42]. However, a much lower incidence has also been indicated [32]. The incidence of cystic ovarian disease has been found to be 8.7% [43]. From the same study evidence was reported that cystic ovarian disease is most frequently (62–85%) characterized by anestrus, as a result of the production of progesterone by more-or-less luteinized cysts [43]. Risk factors for prolonged luteal phases include parity, metritis, problem calvings, retained placenta, general health problems and early ovarian activity [15,16,21,33]. A lot of luteal cysts disappear spontaneously [25]. It has been found that 60% of cows that developed cystic ovaries early after parturition re-established ovarian cycles spontaneously [44]. In consequence, cysts occurring before 30 days after calving are excluded in some studies [43]. Endogenous LH treatment and prostaglandin treatment of luteal cysts can reduce the time until estrus [25]. Further it has been argued that cysts should not be treated until 50 or 60 days after calving, because cysts before that time were considered to have a high self-cure rate and the effect of treatment was considered to be low. Based on these findings, no effect of an inline progesterone indicator in relation to cycle length is assumed for this study.

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The assumed effects of a progesterone indicator did not include benefits from differentiation between types of cysts. Pathological ovarian cysts, which arise as the result of an ovulation of follicles, are usually divided in to two types: (1) follicular cysts and (2) luteal cysts. Follicular cysts may be single or multiple on one or both ovaries, they tend to be thin-walled and have no evidence of luteinization of the granulosa, which results in basal progesterone levels. Luteal cysts are thicker walled follicular structures. Both types of cysts can be considered as different expressions of the same disease. Cysts are dynamic, so that they may persist or regress or even be replaced by others. The herd incidence of ovarian cysts has been reported to be between 5.6% and 18.8% [13]. Higher frequencies have been reported [25]. In another study the relative frequencies of follicular cysts, luteal cysts, and cystic corpus lutea were 65%, 19%, and 16%, respectively [45]. Palpation per rectum can diagnose a cyst but not whether it is a luteal or a follicular cyst. Typically, the treatment against follicular cysts is chosen in all cases, as this is assumed to be the most frequently occurring type of cyst. If a progesterone test was performed, then the type of cyst (high progesterone = luteal cyst; low progesterone = follicular cyst) could be determined and an improved treatment response to luteal cysts would occur. Also, therapy would be more effective if response to the first treatment was known from repeated progesterone measurements [13]. However, based on a review study it has been stressed that there is a high spontaneous recovery rate and that endogenous LH treatment is efficient for both types of cysts with almost 80% exhibiting a fertile estrus within 16–30 days [25]. A spontaneous recovery rate of 20% of follicular cysts has also been reported [46]. Other results have shown that Ovsynch may be efficient if a cyst is diagnosed only by palpation [47]. The consequences of a progesterone indicator for the determination of type of cyst prior to decision on treatment have previously been estimated by simulation modeling [13]. From an economic point of view the benefit was questionable [13]. As ultrasonography [39] or manual progesterone test kits are available, then the benefit from an inline milk progesterone system for determining the type of cyst prior to decision on treatment can not exceed the cost of doing the alternative test in the specific case. It is consequently not considered relevant to include the aspect of differentiation of ovarian cysts in the case of ovarian cysts diagnosed by palpation per rectum in this study. Differentiation of treatment may be omitted, or otherwise the corresponding economic effects relative to using the progesterone kit or ultrasonography could be calculated from the number of cysts expected. It has been suggested that further information can be derived from a progesterone indicator. Current energy status may be indicated by progesterone level. This information might be used in management of the cow and the herd, and energy status may be included as a factor in a number of biological relationships [48,49]. Progesterone level at estrus has also been suggested as an indicator for conception chance at time of AI and as an indication of whether the cow will become a repeat-breeder [50]. Currently, the required parameters and the way the information should be applied in management strategies were considered to be too uncertain to be included in the present simulation study. In summary, a number of potential effects such as: identification of abnormal cycles, typing of cysts, tests for pregnancy and conceptus loss, prediction of conception date (and subsequent calving date) in case of unknown breeding date (e.g. bull breeding), indication of energy balance, indication of conception chance at current estrus, etc. were judged to be either too uncertain or of too little benefit for inclusion in the simulation model.

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5. Conclusion Based on the results from this study it is concluded that the implementation of an inline progesterone indicator in a dairy herd using visual estrus detection has a break-even price of s3–81 per cow-year depending on differences in implementation type and herd differences primarily related to reproductive efficiency. In the default herd the break-even price was s46 per cow-year. These break-even prices do not include any potential labor savings, which might provide additional benefits.

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